





import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
     __A							   =							pd.read_csv("sample_data.csv", header=None)
     __A							   =							df.shape[:1][0]
     # If you're using some other dataset input the target column
     __A							   =							df.iloc[:, 1:2]
     __A							   =							actual_data.values.reshape(len_data, 1)
     __A							   =							MinMaxScaler().fit_transform(actual_data)
     __A							   =							10
     __A							   =							5
     __A							   =							20
     __A							   =							len_data - periods * look_back
     __A							   =							actual_data[:division]
     __A							   =							actual_data[division - look_back :]
     __A   ,      __A							   =							[], []
     __A   ,      __A							   =							[], []

     for i in range(0, len(train_data) - forward_days - look_back + 1):
          train_x.append(train_data[i : i + look_back])
          train_y.append(train_data[i + look_back : i + look_back + forward_days])
     for i in range(0, len(test_data) - forward_days - look_back + 1):
          test_x.append(test_data[i : i + look_back])
          test_y.append(test_data[i + look_back : i + look_back + forward_days])
     __A							   =							np.array(train_x)
     __A							   =							np.array(test_x)
     __A							   =							np.array([list(i.ravel()) for i in train_y])
     __A							   =							np.array([list(i.ravel()) for i in test_y])

     __A							   =							Sequential()
     model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
     model.add(LSTM(64, input_shape=(128, 1)))
     model.add(Dense(forward_days))
     model.compile(loss="mean_squared_error", optimizer="adam")
     __A							   =							model.fit(
         x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
     )
     __A							   =							model.predict(x_test)








'''simple docstring'''






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer






def   __A    (				a_    :	str   ,a_    :	str   ,**a_    :	int						):
		lowerCAmelCase  :     str       						= AutoConfig.from_pretrained(a_   ,**a_						)
		lowerCAmelCase  :     Any       						= AutoModelForSeqaSeqLM.from_config(a_						)
		model.save_pretrained(a_						)
		AutoTokenizer.from_pretrained(a_						).save_pretrained(a_						)
		return model


if __name__ == "__main__":
					fire.Fire(save_randomly_initialized_version)



